ICES Journal of Marine Science: Journal du Conseil Advance Access originally published online on February 5, 2009
ICES Journal of Marine Science: Journal du Conseil 2009 66(6):1119-1129; doi:10.1093/icesjms/fsp009
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
This article appears in the following ICES Journal of Marine Science issue: The Ecosystem Approach with Fisheries Acoustics and Complementary Technologies [View the issue table of contents]
Artificial neural networks for fish-species identification
Instituto Nacional de Investigación y Desarrollo Pesquero (INIDEP), Paseo V. Ocampo Nº 1, Mar del Plata (B7602HSA), Argentina
Correspondence to A. G. Cabreira: tel: +54 223 4862586; fax: +54 223 4861830; e-mail: cabreira{at}inidep.edu.ar
Cabreira, A. G., Tripode, M., and Madirolas, A. 2009. Artificial neural networks for fish-species identification. – ICES Journal of Marine Science, 66: 1119–1129.Acoustic fish detection is a valuable tool for the continuous monitoring of fish schools. However, changes in species composition or mixed multispecies situations still complicate the analysis of acoustic data. Validation of echo recordings is usually accomplished by trawling, but only at point locations. However, species proportions and size distributions in the catch can be biased because of gear selectivity and fish avoidance. In this paper, techniques involving training and testing of artificial neural networks (ANNs) are applied for the automatic recognition and classification of digital echo recordings of schools in the Southwest Atlantic. Energetic, morphometric, and bathymetric school descriptors were extracted from the echo-recordings as the input for the ANNs. Several pelagic and demersal fish species known to aggregate into schools were considered, including anchovy, rough scad, longtail hoki, sprat, and blue whiting. Different types of ANNs were tested. Best performances were obtained by levelling the input data (number of schools) per species. Correct classification rates up to 96% were obtained, depending on the species, type of network, and the number of school descriptors utilized. Some of these species inhabit areas geographically distant from each other. Hence, the contribution of the school position as a descriptor was investigated. By deleting the geographical location of the schools from the ANN input data, the average performance decreased to some extent but was still satisfactory, proving the networks were able usually to recognize fish species based only on the intrinsic characteristics of the school. The results have encouraged further testing of this method as a useful tool for scrutinizing echograms.
Keywords: acoustics, artificial neural network, fish schools, species identification
Received 23 July 2008; accepted 13 November 2008; advance access publication 5 February 2009.